Meta, GM Cut Thousands as AI Skills Gap Widens

Meta began its latest round of layoffs on May 20, 2026, targeting roughly 8,000 employees — about 10% of its global workforce — while General Motors separately cut more than 600 IT workers to replace them with AI-focused hires. The moves, happening within days of each other, illustrate a pattern accelerating across U.S. industry: companies are not simply reducing headcount, they are actively swapping one type of worker for another.

Meta’s Cuts Signal a Structural Shift, Not a One-Time Trim

Meta’s May layoffs are not an isolated event. According to CNBC, two additional rounds are expected this year — one in August and another in the fall — based on accounts from people with knowledge of the matter. That would bring Meta’s total job cuts since late 2022 to well above 25,000, a figure Wired confirmed by tallying announced reductions over the past four years.

CEO Mark Zuckerberg’s stated rationale has shifted considerably since the first wave of cuts. Where early reductions were framed around post-pandemic overcorrection, the current round is explicitly tied to AI investment. A human resources leader at Meta, cited by Wired, described the cuts as necessary to “run the company more efficiently” and “offset the other investments” it is making — a direct reference to AI infrastructure spending.

The financial picture makes the layoffs harder for employees to accept. Meta has reported record-high profits even as it eliminates roles, creating a disconnect that has visibly damaged internal morale.

Inside Meta: Morale at a Historic Low

Wired spoke with 16 current and former Meta employees across multiple roles, all of whom declined to be named under company policy. Their accounts describe an unusually grim internal atmosphere driven by several compounding factors.

Beyond the layoffs themselves, employees cited widening pay gaps, mandatory role changes for hundreds of senior engineers, and the recent installation of monitoring software on company computers — software that, according to those employees, tracks activity for the purpose of training AI models.

“I don’t know anyone having a good time,” a policy staffer told Wired. “The vibe is a bit ‘over it’ — lack of connection to the mission, upcoming layoffs, American employees being used to train the AI models that will replace them.”

Severance terms — a minimum of 16 weeks’ pay and 18 months of paid health care — are reportedly generous enough that many employees are hoping to be cut rather than stay. In the UK, some workers have begun organizing toward union registration, according to Wired.

Only employees working directly on core AI development appear largely unaffected. A longtime senior leader at Meta told Wired that those individuals, along with workers on the best compensation packages, are the only ones “thriving.”

GM’s Deliberate Skills Swap in the Auto Sector

General Motors took a different but structurally similar approach. TechCrunch reported that GM laid off more than 10% of its IT department — approximately 600 salaried employees — explicitly to create budget and headcount room for workers with AI-specific backgrounds.

The roles GM is now recruiting for include:

• AI-native application development
• Data engineering and analytics
• Cloud-based engineering
• Agent and model development
• Prompt engineering
• AI workflow design

GM’s stated intent is to hire people who can build AI systems from the ground up — designing architectures, training models, and engineering data pipelines — not simply use AI tools to augment existing workflows. The company has said it is actively hiring, but TechCrunch noted the exchange will not be one-to-one, meaning net job losses are likely.

The broader automotive picture is starker. CNBC calculated that Ford, GM, and Stellantis combined have cut more than 20,000 U.S. salaried jobs — representing 19% of their combined peak workforces this decade. TechCrunch attributed those reductions broadly to technological change, including AI adoption, though other factors such as slowing EV demand and restructuring also contributed.

Not All Companies Are Cutting: Samsara’s Data Advantage

The pattern is not uniformly negative for workers. TechCrunch highlighted Samsara as an example of a company that built a revenue-generating AI use case rather than simply automating existing roles away.

Samsara spent roughly a decade deploying cameras inside millions of commercial trucks for driver monitoring, theft prevention, and liability documentation. The company used that proprietary data to train its own model capable of detecting road hazards such as potholes — a capability that generates direct value for fleet operators and creates demand for new technical roles rather than eliminating existing ones.

The contrast with the automakers is instructive. TechCrunch noted that accounts from engineers and founders across the industry suggest many companies adopting AI do not yet have a clear plan for what to do with it — a dynamic that can produce layoffs without corresponding productivity or revenue gains.

Automotive Layoffs Extend Beyond GM

CNBC’s Morning Squawk flagged automaker layoffs as one of five key investor items for the week of May 15, placing them alongside macroeconomic events including Jerome Powell’s final day as Federal Reserve chair. The newsletter’s framing — grouping workforce reductions with central bank transitions and U.S.-China trade developments — reflects how prominently AI-driven employment shifts are registering in financial markets.

Ford and Stellantis have not announced AI-specific hiring initiatives comparable to GM’s, though both have disclosed significant salaried workforce reductions in recent quarters. The combined 20,000-plus figure from CNBC covers cuts from recent employment peaks without specifying a single calendar year.

What This Means

The simultaneous moves by Meta and GM in the same week make a pattern visible that has been building for several years: AI is not simply automating tasks at the margin, it is prompting companies to reclassify entire job categories as obsolete while creating demand for a narrower set of technical roles.

The critical detail is the asymmetry. GM’s IT cuts of 600 will not produce 600 AI engineering hires — the company itself acknowledged the exchange will be net-negative. Meta’s layoffs are explicitly framed as funding AI infrastructure investment, not AI hiring. The workers being displaced and the workers being recruited are largely different people with different educational backgrounds, different career trajectories, and different geographic concentrations.

For workers in mid-level IT, operations, and non-technical roles at large companies, the structural risk is real and near-term. The skills most in demand — model development, data pipeline engineering, agent architecture — require years of retraining that severance packages do not fund. The 16-week Meta severance, however generous by corporate standards, does not bridge that gap.

The Samsara example suggests the path forward for companies is building proprietary data assets that justify AI investment with measurable revenue outcomes. For workers, it suggests that proximity to data strategy and AI system design — rather than AI tool use alone — is where employment stability is concentrating.

FAQ

How many jobs is Meta cutting in 2026?

Meta began cutting approximately 8,000 employees — roughly 10% of its workforce — starting the week of May 20, 2026, according to CNBC. Two additional layoff rounds are expected later in 2026, one in August and one in the fall.

Why is GM laying off IT workers if it says AI is a priority?

GM is replacing generalist IT roles with workers who have AI-specific skills — including model development, data engineering, and agent design — rather than eliminating AI investment. TechCrunch reported the swap will likely result in net job losses because the new hires will not match the number of workers cut.

Which AI skills are companies hiring for right now?

Based on GM’s publicly stated recruiting priorities, the most sought-after capabilities are AI-native development, data engineering and analytics, cloud-based engineering, agent and model development, prompt engineering, and AI workflow design — roles focused on building AI systems rather than using existing tools.